Mast taxonomy

MAST is a taxonomy of how multi-agent LLM systems fail, from the Berkeley paper "Why Do Multi-Agent LLM Systems Fail?" (arXiv:2503.13657). The authors annotated over 1,000 execution traces from seven open-source multi-agent frameworks and grouped what went wrong into 14 failure modes under three categories: system design issues, inter-agent misalignment, and task verification. Expert annotators agreed on the labels with a Cohen's kappa of 0.88.

Most of the 14 modes live in the interaction between agents, not in any one agent's output. A step gets repeated, a task derails at a handoff, an agent stops before the work is done, a result gets accepted without a check. If you grade each agent's answer on its own, none of that shows up. That's why the paper works from traces rather than outputs, and it's the reason to evaluate a multi-agent system at the trace level.

In practice you use MAST to classify a failed production trace. Walk the trace step by step. At each handoff, check what one agent passed and what the next one actually used. At each stop, check what verified the result. The mode you land on tells you where the fix goes: a design issue means a prompt or role change, a misalignment means changing what agents pass each other, a verification failure means a missing check.